Study of time-frequency representations in the presence of heteroscedastic dependent noise
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Updated
May 29, 2022 - R
Study of time-frequency representations in the presence of heteroscedastic dependent noise
This python binding for run OpenCL kernel code of SFEGO is mimic of Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) and this project achieve 10000x faster than MEEMD. Also the result is better than Bi-dimensional Empirical Mode Decomposition. (BEMD)
This is a preliminary implementation of an empirical mode decomposition algorithm in C#.
Paper reproduction: Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert–Huang Transform Applied to Depth of Anaesthesia
Clone of https://bitbucket.org/luukko/libeemd.git with decent Autotools support for building correctly on Windows, macOS, and others.
PyTorch Wrapper for Earth-Mover-Distance (EMD) for 3D point cloud regression
2016년 엘스비어 논문을 통해 신호 분석에 특화된 EMD, EEMD의 효과를 ARIMA를 통해 확인한다.
Exemplo de aplicação do método EMD (empirical mode decomposition) apresentado na disciplina "Introdução à Ciência de Dados" pelo professor Rodrigo Mello em 2019, em São Carlos-SP
Wire-cut CNC machine (EDM and stone). STM32 project for TrueStudio
Comparison of visual odometry transformation reconstruction methods for optimal path estimates and localization using feature detection, description, matching and trajectory generation
some misc tools for audio processing and etc.
Rust Optimal Transport solvers
Image classification and unsupervised learning using latent space vectors produced by convolutional neural nets together with the original vectors space
Analysis scripts accompanying "Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics" https://doi.org/10.1101/2021.04.12.439547
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